The excessive use of vaccines, antibiotics, and other preventive therapeutic agents in conventional broiler production has resulted in the frequent occurrence of antibiotic residues in edible tissues, posing risks to both animal and human health. These residues contribute to the global problem of antimicrobial resistance, highlighting the need for alternative approaches to poultry disease prevention. This study aimed to evaluate whether a comprehensive preventive programme encompassing biosecurity, hygiene, and an antibiotic-free prophylaxis regimen could eliminate the need for prophylactic antibiotic therapy, thereby enabling the production of broiler meat free of antibiotic residues. This approach was contrasted with the conventional production system, in which less rigorous preventive practices necessitate the prophylactic use of broad-spectrum antibiotics, ultimately resulting in detectable residues within the muscle and liver tissues of conventionally reared chickens. Ross 308 broilers were reared under field conditions. in. The control group was reared using conventional prophylactic protocols, including broad-spectrum antibiotics, vitamins, bio-stimulants, mineral supplements, and acidifiers via drinking water. The experimental group received no antibiotics; and instead were treated with hydro-soluble probiotic preparations containing vitamin C, lactose, fructose, and baker’s yeast, combined with continuous water disinfection throughout the fattening period. Microbiological analyses showed the presence of antibiotic residues in the soft tissues of conventionally reared broilers, while no residues were detected in the experimental group. Production performance indicators—mortality rate, vitality, final body weight, and overall cost—were similar to or superior to those in the antibiotic-free group, confirming that broiler meat production without antibiotics is both feasible and economically viable.

Explainable AI (XAI) is essential for building trust in Deep Neural Networks (DNNs). SHAP (SHapley Additive exPlanations) is a well‐known XAI technique for attributing feature importance, but it struggles with exponential computational complexity as the number of features increases. Various approximation methods have been suggested, but they compromise SHAP's theoretical principles. We introduce AA‐SHAP, a novel approach that derives superpixel affinity from the explained model's internals to identify and group superpixels. AA‐SHAP constructs a relevance‐consistency affinity between superpixel interdependence, enabling much faster SHAP calculations on a reduced set of meta‐superpixels while outperforming previous methods in explanation faithfulness. Exact Shapley values are computed on the reduced meta‐superpixel game, preserving all axiomatic guarantees within the aggregated feature space. Evaluated across multiple datasets, both convolutional and transformer classification architectures show that AA‐SHAP produces more faithful attributions than competing methods while improving computational speed and maintaining SHAP's theoretical axioms. The source code is available at https://github.com/vhasic/AA‐SHAP .

BACKGROUND Candida auris is a widely distributed yeast that is considered a dangerous pathogen, with reported mortality rates ranging from 30% to 60%. This yeast shows a high level of resistance to several antifungal agents commonly used to treat systemic infections. The pathogen persists on contaminated surfaces, tolerates hospital-grade disinfectants, survives desiccation and spreads easily through direct or indirect contact. It has been reported on all five continents and is increasingly prevalent in Europe. OBJECTIVE To determine the distribution and antifungal susceptibility/resistance of Candida auris isolates identified in Europe until January 2025. METHODS This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Searches were conducted in EBSCOhost, MEDLINE/PubMed, Scopus and SciELO databases using the terms 'Candida auris' and 'Candidozyma auris', combined with the name of each European country. It was limited to English or Spanish articles published until 31 January 2025, excluding reviews, meta-analyses and book chapters. RESULTS Ninety-one articles reporting antifungal susceptibility were retrieved, covering 2191 clinical isolates of C. auris from 16 countries. Most isolates were from Spain (n = 886, 40.44%), Italy (n = 553, 25.24%), Greece (n = 214, 9.77%), the United Kingdom (n = 182, 8.31%) and Russia (n = 108, 4.93%), accounting for 88.68% of cases. The remaining 248 isolates (11.32%) were reported across 11 other countries. Fluconazole resistance was found in 90.51% (1555/1718), while resistance to amphotericin B and echinocandins was 13.17% (223/1693) and 4.57% (76/1693), respectively. CONCLUSIONS Candida auris has been predominantly detected in Southern Europe, where the majority of clinical isolates exhibit resistance to fluconazole. Consensus is essential for timely diagnosis, targeted treatment and infection control to prevent its spread. New therapeutic options must be explored to manage Candida auris.

BackgroundThe comet assay is a sensitive and widely used technique for assessing DNA damage at the single-cell level. Despite its advantages, traditional manual scoring methods remain time-consuming, subjective and limited in scalability, posing challenges for high-throughput and standardized analysis.ObjectiveThis study aims to develop and evaluate a deep learning-based system for automated comet assay image classification, addressing limitations of manual and semi-automated approaches while enhancing accuracy, reproducibility and processing efficiency.MethodA YOLOv5-based object detection model was trained on a dataset of 875 annotated comet assay images, curated through a three-step expert-reviewed process. Various hyperparameters and data augmentation techniques were optimized to improve performance. The dataset was split into training, validation and test sets, and model performance was evaluated using mAP, precision, recall and confusion matrix analysis.ResultsThe model achieved strong performance, with mAP@0.5 reaching 0.98 and recall exceeding 0.8. Detailed analyses revealed robust learning behavior and generalization capacity. Visual outputs, including precision-recall curves and class-wise confusion matrices, confirmed high classification accuracy, although overlapping comet structures and class imbalance posed challenges. The model demonstrated improved scalability and processing speed compared to traditional tools, supporting its integration into web-based applications.ConclusionThe proposed YOLOv5-based system offers a scalable and accurate solution for automating comet assay analysis. It significantly enhances throughput and reduces human error, supporting its application in genotoxicity testing, biomonitoring and molecular epidemiology. Future work will focus on handling overlapping structures, benchmarking against existing tools and optimizing deployment in real-world laboratory settings.

The purpose of this paper is to develop machine learning (ML) models for prediction of surface roughness and cutting forces of 42CrMo4 steel in hard turning process. A full factorial experimental design with four input parameters: cutting speed, depth of cut, feed and insert radius was used to develop ML models for predicting the performance of turning process. The backward linear regression, random forest (RF) and XGBoost were used. Also, for the linear regression model and for the best RF and XGBoost model five-fold cross validation was done to confirm that the models provide reliable generalization estimates rather than performance dependent on a single data split. The XGBoost model demonstrates the most compact clustering of residuals with fewer large errors, indicating better overall stability and predictive consistency compared to the linear regression and RF models. The application of different ML methods with monitoring of standardized residuals on unseen data confirms the reliability of the developed models in real application conditions. This study provides a structured and comparative modeling framework across multiple output variables, where backward linear regression, RF and XGBoost models were developed. Several architectural and hyperparameter variations of the RF and XGBoost models were evaluated to ensure optimal configuration for each output. Also, variable influence was examined through permutation feature importance for ensemble models and statistical significance testing for linear regression, enabling interpretation and discussion of the influence of input variables on selected outputs.

The AMY2B gene encodes pancreatic amylase, a critical enzyme for starch digestion. While previous studies have examined AMY2B copy number variation (CNV) in domestic and some wild animals, less is known about wild carnivores inhabiting regions with limited anthropogenic starch exposure. We analyzed blood samples for serum amylase activity and copy number variation in AMY2B gene from 8 wolves (Canis lupus), 11 brown bears (Ursus arctos), and 3 red foxes (Vulpes vulpes) from Bosnia and Herzegovina. AMY2B gene copy number was assessed using droplet digital PCR (ddPCR), and serum amylase activity and glucose levels were quantified. Although the number of fox samples was limited, foxes and wolves consistently harbored two copies of AMY2B, while brown bears exhibited higher CNV (3.67–8.40, mean 5.88). Serum amylase activity was highest in foxes, moderate in wolves, and variable but lower in bears. Despite differences in AMY2B copy number and serum amylase activity, circulating glucose concentrations did not differ significantly among species. Our findings suggest that variation in AMY2B copy number among wild carnivores may be associated with species-specific evolutionary histories and dietary adaptations, providing insight into genomic mechanisms underlying carbohydrate utilization in natural populations.

A proper diet that provides balanced nutrients according to species, breed, age, sex, and purpose is essential for achieving optimal animal growth, development, and physiological function. In relation to this statement, the aim of this study was to quantitatively assess the effects of different dietary regimens on the morphometric characteristics of the small intestine of late puerperal rats, with a emphasis on the duodenum and jejunum. The research included measurements of the height and width of intestinal villi, and the depth of crypts, in order to determine specific morphological changes associated with the influence of the diet. The experiment involved 18 adult rats divided into three groups: the first group received standard commercial rat feed (control group), the second group was fed bakery products, and the third group was given a diet consisting exclusively of meat. Over a 49-day period, body weight, organ weights, and intestinal segment lengths were measured. Histological analyses of duodenum and jejunum samples were performed, alongside detailed morphometric assessment of the intestinal mucosa. Significant alterations in intestinal villi were observed. In the duodenum, the greatest villus height and width, as well as crypt depth, were observed in rats that had meat-based diet. Similarly, in the jejunum, rats from the same group exhibited the greatest villus height and crypt depth, while the mean measure of the villus width was almost identical to mean measure in rats that were fed with commercial food. These findings suggest that diet composition profoundly influences intestinal architecture and function. Overall, the study emphasizes the critical role of balanced nutrition in maintaining gastrointestinal health, systemic homeostasis, and optimal development, not only in laboratory animals but also in a broader veterinary and biomedical context.

Acidic wastewater generated during sulfuric acid leaching of reduced tionite within the EUROTITAN process was treated using three low-cost adsorbents: fly ash, bentonite, and red mud slag. Tionite is a solid residue originating from the sulfate route of TiO2 production, whereas the investigated wastewater is a secondary acidic stream produced during hydrometallurgical treatment of reduced tionite. The initial wastewater was characterized by low pH and elevated concentrations of Fe, Al, Ti, B, Cu, Mn, Pb, Cr, and Li. Batch adsorption experiments were carried out by varying contact time from 4 to 24 h and adsorbent dosage from 5 to 15 g/L. The results showed distinct selectivity depending on adsorbent type and solution chemistry. Bentonite exhibited the most stable performance, achieving nearly complete removal of Pb, Cu, B, and Li, while Fe and Al were only partially removed and Ti removal remained limited. Fly ash showed high affinity toward Pb and Cu, but its performance was strongly affected by dosage and contact time. Red mud slag demonstrated excellent Pb removal, high Cu removal, and time- and dosage-dependent Ti removal, although partial dissolution of Fe- and Al-bearing phases occurred under strongly acidic conditions. Overall, the results confirm that industrial by-products and natural clay materials can contribute to partial purification of acidic metallurgical wastewater, while additional neutralization or polishing steps are required for complete treatment.

Purpose: To review the potential relationship between vitamin B12 and colorectal cancer, with particular emphasis on its involvement in one-carbon metabolism and epigenetic regulation.Methods: This narrative review summarises current evidence on the biological mechanisms and epidemiological associations linking vitamin B12 status with colorectal cancer risk.Results: Available evidence indicates a biologically plausible but epidemiologically inconsistent relationship between vitamin B12 and colorectal cancer. Studies assessing dietary intake frequently report an inverse association, particularly for rectal cancer, whereas analyses of circulating vitamin B12 concentrations yield heterogeneous, and often conflicting findings.Conclusion: Vitamin B12 does not act as an isolated protective factor but as part of a complex metabolic and nutritional system. Although its potential protective role in colorectal carcinogenesis is biologically based, it remains insufficiently confirmed, emphasising the need for further integrated research.

Introductory programming courses remain challenging for many students, which motivates educators to adopt gamification to enhance engagement and learning. More recent work explores adaptive gamification, where game elements and task flow are tailored to individual learners. A key requirement for such adaptation is the ability to predict student success on upcoming tasks. Using a dataset of task attempts collected from a gamified introductory programming activity, we examine the predictive value of coarse-grained knowledge components, task difficulty, and dynamic student performance features. The results show that behavioral signals are substantially more informative than task properties: a student's prior success history and their position within a lesson sequence are the strongest predictors of future correctness. Although advanced topics such as file handling and structures are associated with increased failure rates, their impact is secondary to students' evolving engagement patterns. These findings highlight the role of momentum and practice effects in gamified programming environments and suggest that adaptive systems should prioritize real-time learner progression when providing instructional support. Dataset and the code for our experiments is available at https://osf.io/cajby.